—Uncertainty is a major barrier in knowledge discovery from complex problem domains. Knowledge discovery in such domains requires qualitative rather than quantitative analysis. Therefore, the quantitative measures can be used to represent uncertainty with the integration of various models. The Bayesian Network (BN) is a widely applied technique for characterization and analysis of uncertainty in real world domains. Thus, the real application of BN can be observed in a broad range of domains such as image processing, decision making, system reliability estimation and PPDM (Privacy Preserving in Data Mining) in association rule mining and medical domain analysis. BN techniques can be used in these domains for prediction and decision support. In this article, a discussion on general BN representation, draw inferences, learning and prediction is followed by applications of BN in some specific domains. Domain specific BN representation, inferences and learning process are also presented. Building upon the knowledge presented, some future research directions are also highlighted.
—Uncertainty, knowledge discovery, Bayesian network, image processing, decision making, privacy preservation, system reliability estimation.
Khalid Iqbal is with the Department of Computer Science and Technology, School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, P.R. China. He is also with the Department of Computer Science, COMSATS Institute of Information Technology, Attock Campus, Pakistan (e-mail: firstname.lastname@example.org).
Xu-Cheng Yin is with the Department of Computer Science and Technology, School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, P.R. China (corresponding author; tel.: +8613810223098, fax: +8610-62332873, e-mail: email@example.com).
Hong-Wei Hao is with Institute of Automation, Chinese Academy of Sciences, Beijing 100190, P.R. China (e-mail: firstname.lastname@example.org). Qazi Mudassar Ilyas is with College of Computer Sciences and Information Technology, King Faisal University, Saudi Arabia (e-mail: email@example.com).
Hazrat Ali is with the Department of Communication Engineering, School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, P. R. China. He is also with the Machine Learning Group, Department of Computer Science, City University London, United Kingdom (e-mail: firstname.lastname@example.org).
Cite:Khalid Iqbal, Xu-Cheng Yin, Hong-Wei Hao, Qazi Mudassar Ilyas, and Hazrat Ali, "An Overview of Bayesian Network Applications in Uncertain Domains," International Journal of Computer Theory and Engineering vol. 7, no. 6, pp. 416-427, 2015.